Survivable network design of all-optical network.

Kwok-Shing Ho.Thesis (M.Phil.)--Chinese University of Hong Kong, 2002.Includes bibliographical references (leaves 69-71).Abstracts in English and Chinese.List of Figures --- p.viList of Tables --- p.viiChapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Overview --- p.1Chapter 1.2 --- Thesis Objectives --- p.6Chapter 1.3 --- Outline of Thesis --- p.8Chapter Chapter 2 --- The Spare Capacity Planning Problem --- p.9Chapter 2.1 --- Mathematical Model of the Spare Capacity Planning Problem --- p.12Chapter 2.1.1 --- Variable Definitions --- p.12Chapter 2.1.2 --- Objective Function and Constraints --- p.15Chapter 2.1.3 --- Complexity --- p.17Chapter 2.2 --- Greedy Algorithm - Spare Capacity Allocation and Planning Estimator (SCAPE) --- p.19Chapter 2.2.1 --- Working Principle of SCAPE --- p.20Chapter 2.2.2 --- Implementation of SCAPE --- p.22Chapter 2.2.3 --- Improved SCAPE --- p.23Chapter 2.3 --- Experimental Results and Discussion --- p.27Chapter 2.3.1 --- Experimental Platform --- p.27Chapter 2.3.2 --- Experiment about Accuracy of SCAPE --- p.27Chapter 2.3.3 --- Experiment about Minimization of Network Spare Capacity --- p.30Chapter 2.3.4 --- Experiment about Minimization of Network Spare Cost --- p.35Chapter 2.4 --- Conclusions --- p.38Chapter Chapter 3 --- Survivable All-Optical Network Design Problem --- p.39Chapter 3.1 --- Mathematical Model of the Survivable Network Design Problem --- p.42Chapter 3.2 --- Optimization Algorithms for Survivable Network Design Problem --- p.44Chapter 3.2.1 --- Modified Drop Algorithm (MDA) --- p.45Chapter 3.2.1.1 --- Drop Algorithm Introduction --- p.45Chapter 3.2.1.2 --- Network Design with MDA --- p.45Chapter 3.2.2 --- Genetic Algorithm --- p.47Chapter 3.2.2.1 --- Genetic Algorithm Introduction --- p.47Chapter 3.2.2.2 --- Network Design with GA --- p.48Chapter 3.2.3 --- Complexity of MDA and GA --- p.51Chapter 3.3 --- Experimental Results and Discussion --- p.52Chapter 3.3.1 --- Experimental Platform --- p.52Chapter 3.3.2 --- Experiment about Accuracy of MDA and GA --- p.52Chapter 3.3.3 --- Experiment about Principle of Survivable Network Design --- p.55Chapter 3.3.4 --- Experiment about Performance of MDA and GA --- p.58Chapter 3.4 --- Conclusions --- p.62Chapter Chapter 4 --- Conclusions and Future Work --- p.63Appendix A The Interference Heuristic for the path restoration scheme --- p.66Bibliography --- p.69Publications --- p.7

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Copyright @ Elsevier Ltd. All rights reserved.In this paper, two bio-inspired Quality of Service (QoS) multicast algorithms are proposed in IP over dense wavelength division multiplexing (DWDM) optical Internet. Given a QoS multicast request and the delay interval required by the application, both algorithms are able to find a flexible QoS-based cost suboptimal routing tree. They first construct the multicast trees based on ant colony optimization and artificial immune algorithm, respectively. Then a dedicated wavelength assignment algorithm is proposed to assign wavelengths to the trees aiming to minimize the delay of the wavelength conversion. In both algorithms, multicast routing and wavelength assignment are integrated into a single process. Therefore, they can find the multicast trees on which the least wavelength conversion delay is achieved. Load balance is also considered in both algorithms. Simulation results show that these two bio-inspired algorithms can construct high performance QoS routing trees for multicast applications in IP/DWDM optical Internet.This work was supported in part ny the Program for New Century Excellent Talents in University, the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1, the National Natural Science Foundation of China under Grant no. 60673159 and 70671020, the National High-Tech Reasearch and Development Plan of China under Grant no. 2007AA041201, and the Specialized Research Fund for the Doctoral Program of Higher Education under Grant no. 20070145017

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